license: llama2
metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: Code Millenials
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 0.8048
verified: false
Bud Code Millenials 34B
Welcome to our Code Model repository! Our model is specifically fine-tuned for code generation tasks. Bud Millenial Code Gen open-source models are currently the State of the Art (SOTA) for code generation, beating all the existing models of all sizes. We have achieved a HumanEval value of 80.48 @ Pass 1, beating proprietary models like Gemini Ultra, Claude, GPT-3.5 etc. by a large margin, and on par with GPT-4 (HumanEval ~ 82. Ref. WizardCoder). Our proprietary model (Bud Code Jr) beats GPT-4 as well with a HumanEval value of 88.2 & a context size of 168K, we will be releasing an API for Researchers, Enterprises, and potential Partners by January 2024 end. If interested, please reach out to [email protected]
News 🔥🔥🔥
- [2024/01/03] We released Code Millenials 34B , which achieves the 80.48 pass@1 on the HumanEval Benchmarks.
- [2024/01/02] We released Code Millenials 13B , which achieves the 76.21 pass@1 on the HumanEval Benchmarks.
HumanEval
For the millenial models, the eval script in the github repo is used for the above result.
Note: The humaneval values of other models are taken from the official repos of WizardCoder, DeepseekCoder, Gemini etc.
Models
| Model | Checkpoint | HumanEval (+) | MBPP (+) |
|---|---|---|---|
| Code Millenials 34B | HF Link | 80.48 (75) | 74.68 (62.9) |
| Code Millenials 13B | HF Link | 76.21 (69.5) | 70.17 (57.6) |
🚀 Quick Start
Inference code using the pre-trained model from the Hugging Face model hub
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("budecosystem/code-millenials-34b")
model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-34b")
template = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
### Instruction: {instruction} ### Response:"""
instruction = <Your code instruction here>
prompt = template.format(instruction=instruction)
inputs = tokenizer(prompt, return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0]))
Training details
The model is trained of 16 A100 80GB for approximately 50hrs.
| Hyperparameters | Value |
|---|---|
| per_device_train_batch_size | 16 |
| gradient_accumulation_steps | 1 |
| epoch | 3 |
| steps | 2157 |
| learning_rate | 2e-5 |
| lr schedular type | cosine |
| warmup ratio | 0.1 |
| optimizer | adamw |
| fp16 | True |
| GPU | 16 A100 80GB |
Important Note
- Bias, Risks, and Limitations: Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding.